There are numerous techniques available for image categorization. Many machine learning algorithms rely on a distance metric for the input of data patterns. Distance metric learning (DML) provides a distance metric for input space of data from a given collection of pairs. The given collection of pairs contains similar or dissimilar points that preserve the distance relation among the training data.
Providing a good distance metric in feature space is crucial in real-world application. Good distance metrics are important to many computer vision tasks, such as an image classification and a content-based image retrieval. The distance metric is explicitly learned to minimize a distance between data points with equivalence constraints and maximize the distance between data points in inequivalence constraints.
DML aims to construct an appropriate distance metric for a given learning task. For example, image categorization, which facilitates semantic-level image retrieval by classifying a set of unlabeled images into pre-defined classes, has benefited from such a technique. However, the application of DML in the multimedia domain frequently encounters problems in terms of both computation and performance due to high-dimensional features space. Specifically, computational costs of many DML algorithms scale at least quadratically with respect to the dimensionality of the feature space and will therefore be relatively expensive when feature dimensions are high, potentially leading to an inaccurate classification. Therefore, there is a need for a metric learning algorithm which will significantly reduce the computational cost of metric learning methods and improve their performance.